Introduction

1 2 3 4 5 6

Key Questions

  • How does HIV persist and migrate across CNS and peripheral tissues despite systemic viral suppression?

Overview

This presentation summarizes:

  • Viral phylogenetics & BEAST modeling
  • CNS reservoir compartmentalization
  • Migration dynamics across tissues & BBB



Cohort Workflow

Last Gift Cohort

Body Donation

Autopsy

Tissue Collection

HIV Sequencing

Phylogenetics and Modeling

OBJECTIVES

Blood Brain Interface

Image attribution: BioRender.
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Objectives

Aim 1

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Aim 2

  • Identify CNS-specific clusters (reservoirs)
  • Characterize:
    • Composition
    • Timing
    • Renewal from periphery
    • Reseeding capacity into peripheral reservoirs

APPROACH

Aim 1

Data Processing

  • SGA HIV env clones across tissues
  • CSF biomarkers & clinical metadata
  • BEAST diffusion + phylogenetic modeling
  • Negative Binomial & Poisson models

Workflow

Aim 2

Data Processing

  • HIV env clones across participants & tissues
  • Cleanup & alignment
  • Phylogenetic reconstruction
  • Identify CNS-specific supported clusters
  • Bayesian skyline / skygrid diffusion models
  • Timing (tMRCA) of clusters
  • Migration flows between CNS clusters & other tissues
  • Modeling factors associated with migration

Workflow

Sampling

Note

  • Our analyses included 20 participants with sequences from both the CNS and Periphery.
  • A total of 1875 SGA clones were included from 49 distincts tissues.
  • Luminex CSF data available for 34 markers.

AIM 1: Viral Dynamics

Maximum Clade Credibility Tree

Time-Scaled Maximum Clade Credibility Tree

HIV Migration Networks

HIV Flow across the BBB through time

AIM 1: Reservoirs Dynamic and CSF Markers

Summary

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Approach

  • We compared a patient-level aggregated negative-binomial model (AGG-NB) with a state-level hierarchical negative-binomial model (STATE-NB) that accounted for variation across individual BEAST states.

Data Preparation

  • Aggregated (pid-level counts):We first fit an aggregated-count model (mod_agg_nb), where counts were summed across BEAST states for each participant.”
  • Longitudinal (state-level / transition-level): We then fit a state-level model (mod_long_nb), leveraging the full longitudinal BEAST reconstruction.

Exposure (log)

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Data Input

Exposure

Conceptual motivation

In a transition model, exposure represents the “opportunity” for observing a migration event between compartments. We considered the following definition:

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Equation

\[ n_{\text{states}, i} : \text{number of Markov states in run } i \times \text{number of sequences from CNS} \times \text{number of sequences from Periphery} \]

Model Code

data %>%
  mutate(
    pairs_cns    = ntissues_cns * (ntissues_cns - 1) / 2,
    pairs_periph = ntissues_periph * (ntissues_periph - 1) / 2,
    
    exposure = case_when(
      migration_type == "cross_BBB" & direction == "CNS to Periph" ~ 
        n_states * nseq_cns * nseq_periph,
      
      migration_type == "cross_BBB" & direction == "Periphery to CNS" ~ 
        n_states * nseq_periph * nseq_cns,
      
      migration_type == "within_CNS" ~ 
        n_states * pairs_cns * (nseq_cns^2),
      
      migration_type == "within_peripheral" ~ 
        n_states * pairs_periph * (nseq_periph^2)
    )
  )

CSF Markers

Data Preparation for Migration Models

Summaries

  • Count migration/transition events from and toward the CNS and **within* CNS
  • Compute number of discrete viral states per run
  • Count SGA clones sampled in CNS and in Periphery

Exposure Definition

  • Aggregated: # states × # CNS sequences × # Periphery sequences
  • long format: # sequences from × # sequences to

Filtering

  • Exclude viremic individuals
  • Exclude individuals without CSF data

Data Transformations

  • Log-transform exposure
  • Log1p-transform CSF marker values and reservoir measures

Workflow

Model Specification: Negative Binomial GLMM

Notes

  • Outcome: count of CNS-related migration/transition events
  • Offset: log(exposure)
  • Predictor: log1p-transformed CSF marker
  • Random effect for participants

Equation

\[ \log(\mu_i) = \beta_0 + \beta_1 \cdot \text{Marker}_i + b_{\text{pid}(i)} + \log(\text{Exposure}_i) \]

fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}
fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
      dna_level_from + dna_level_to +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}

Model Results: Aggregated (pid-level counts)

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok 1.037 0.7983–1.35 0.787
GM-CSF 8 Ok 0.888 0.7500–1.05 0.170
GRO-alpha (CXCL1) 13 Ok 0.988 0.9811–1.00 0.001 **
IL-1α 1 Sparse Data 0.154 0.0023–10.37 0.384
IL-1β 5 Ok 0.805 0.6742–0.96 0.017 *
IL-1RA 12 Ok 1.000 0.9995–1.00 0.000 ***
IL-2 2 Sparse Data 0.424 0.2993–0.60 0.000 ***
IL-5 5 Ok 0.986 0.8114–1.20 0.891
IL-6 12 NA Estimates 2.156 NA–NA NaN
IL-7 13 Ok 0.351 0.0617–2.00 0.238
IL-8 (CXCL8) 13 Ok 0.998 0.9953–1.00 0.047 *
IL-9 1 Sparse Data 1.094 0.5815–2.06 0.781
IL-10 8 Ok 0.487 0.2609–0.91 0.024 *
IL-15 5 Ok 0.459 0.1696–1.24 0.126
IL-17A/CTLA-8 1 Sparse Data 0.415 0.0560–3.07 0.389
IL-18 13 Ok 1.009 0.9873–1.03 0.434
IL-31 1 Sparse Data 0.930 0.6767–1.28 0.652
IP-10 (CXCL10) 13 Ok 1.000 0.9967–1.00 0.828
MCP-1 (CCL2) 13 Ok 1.000 0.9983–1.00 0.915
MIP-1α (CCL3) 13 Ok 0.867 0.7731–0.97 0.015 *
MIP-1β (CCL4) 13 Ok 0.964 0.9421–0.99 0.002 **
RANTES (CCL5) 13 Ok 1.007 0.9203–1.10 0.882
SDF-1α 13 Ok 1.000 0.9993–1.00 0.839
TNF-α 2 Sparse Data 0.333 0.2189–0.51 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 0.977 0.857–1.11 0.733
GM-CSF 10 Ok 0.864 0.769–0.97 0.015 *
GRO-alpha (CXCL1) 15 Ok 0.987 0.981–0.99 0.000 ***
IL-1α 1 Sparse Data 0.252 0.013–4.88 0.362
IL-1β 6 Ok 0.858 0.734–1.00 0.054 .
IL-1RA 14 Ok 1.000 1.000–1.00 0.003 **
IL-2 2 Sparse Data 0.374 0.301–0.47 0.000 ***
IL-5 7 Ok 0.871 0.667–1.14 0.312
IL-6 14 Ok 0.998 0.997–1.00 0.000 ***
IL-7 15 Ok 1.616 0.433–6.03 0.475
IL-8 (CXCL8) 15 Ok 0.999 0.998–1.00 0.093 .
IL-9 1 Sparse Data 1.201 0.682–2.11 0.526
IL-10 9 Ok 0.871 0.712–1.07 0.179
IL-15 7 Ok 0.298 0.143–0.62 0.001 **
IL-17A/CTLA-8 2 Sparse Data 0.725 0.256–2.05 0.544
IL-18 15 Ok 1.017 1.000–1.03 0.047 *
IL-31 1 Sparse Data 0.917 0.701–1.20 0.528
IP-10 (CXCL10) 15 Ok 1.000 0.999–1.00 0.569
MCP-1 (CCL2) 15 Ok 1.000 0.999–1.00 0.881
MIP-1α (CCL3) 15 Ok 0.898 0.831–0.97 0.007 **
MIP-1β (CCL4) 15 Ok 0.984 0.967–1.00 0.071 .
RANTES (CCL5) 15 Ok 0.993 0.929–1.06 0.830
SDF-1α 15 Ok 1.000 0.999–1.00 0.272
TNF-α 3 Sparse Data 0.381 0.234–0.62 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.038 0.8740–1.23 0.671
GM-CSF 10 Ok 0.874 0.7675–0.99 0.041 *
GRO-alpha (CXCL1) 15 Ok 0.989 0.9818–1.00 0.002 **
IL-1α 1 Sparse Data 0.310 0.0072–13.34 0.542
IL-1β 6 Ok 0.852 0.6821–1.07 0.160
IL-1RA 14 Ok 1.000 0.9995–1.00 0.000 ***
IL-2 2 Sparse Data 0.388 0.3060–0.49 0.000 ***
IL-5 7 Ok 0.918 0.7765–1.08 0.313
IL-6 14 Ok 0.998 0.9973–1.00 0.000 ***
IL-7 15 Ok 1.402 0.3160–6.22 0.657
IL-8 (CXCL8) 15 Ok 0.999 0.9970–1.00 0.212
IL-9 1 Sparse Data 1.129 0.6593–1.93 0.658
IL-10 9 Ok 0.872 0.6350–1.20 0.399
IL-15 7 Ok 0.319 0.1448–0.70 0.005 **
IL-17A/CTLA-8 2 Sparse Data 0.971 0.2688–3.51 0.964
IL-18 15 Ok 1.014 0.9963–1.03 0.119
IL-31 1 Sparse Data 0.965 0.7235–1.29 0.807
IP-10 (CXCL10) 15 Ok 1.000 0.9986–1.00 0.715
MCP-1 (CCL2) 15 Ok 1.000 0.9989–1.00 0.950
MIP-1α (CCL3) 15 Ok 0.875 0.7832–0.98 0.017 *
MIP-1β (CCL4) 15 Ok 0.984 0.9608–1.01 0.202
RANTES (CCL5) 15 Ok 1.012 0.9314–1.10 0.780
SDF-1α 15 Ok 1.000 0.9984–1.00 0.963
TNF-α 3 Sparse Data 0.362 0.2447–0.54 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.028 8.2e-01–1.290 0.810
GM-CSF 10 Ok 0.892 7.2e-01–1.104 0.292
GRO-alpha (CXCL1) 15 Ok 0.986 9.8e-01–0.997 0.010 *
IL-1α 1 Sparse Data 0.001 1.8e-05–0.054 0.001 ***
IL-1β 6 Ok 0.875 6.8e-01–1.120 0.289
IL-1RA 14 Ok 0.999 1.0e+00–1.000 0.000 ***
IL-2 2 Sparse Data 0.410 2.4e-01–0.705 0.001 **
IL-5 7 Ok 1.207 8.2e-01–1.769 0.334
IL-6 14 Ok 0.998 1.0e+00–1.000 0.044 *
IL-7 15 Ok 0.362 8.1e-02–1.629 0.186
IL-8 (CXCL8) 15 Ok 0.999 1.0e+00–1.002 0.507
IL-9 1 Sparse Data 0.560 2.6e-01–1.217 0.143
IL-10 9 Ok 1.004 7.0e-01–1.434 0.983
IL-15 7 Ok 0.231 6.2e-02–0.861 0.029 *
IL-17A/CTLA-8 2 Sparse Data 1.644 2.4e-01–11.447 0.616
IL-18 15 Ok 1.013 9.8e-01–1.043 0.376
IL-31 1 Sparse Data 0.881 6.4e-01–1.213 0.437
IP-10 (CXCL10) 15 Ok 1.000 9.9e-01–1.006 0.968
MCP-1 (CCL2) 15 Ok 0.999 1.0e+00–1.001 0.236
MIP-1α (CCL3) 15 Ok 0.915 8.0e-01–1.046 0.191
MIP-1β (CCL4) 15 Ok 0.971 9.4e-01–1.007 0.117
RANTES (CCL5) 15 Ok 0.914 8.1e-01–1.032 0.145
SDF-1α 15 Ok 1.000 1.0e+00–1.001 0.663
TNF-α 3 Sparse Data 0.446 2.0e-01–1.003 0.051 .
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

Model Results: Longitudinal / state-level data

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok 1.043 0.921–1.18 0.507
GM-CSF 8 Ok 0.927 0.859–1.00 0.049 *
GRO-alpha (CXCL1) 13 Ok 0.996 0.992–1.00 0.047 *
IL-1α 1 Sparse Data 0.077 0.014–0.42 0.003 **
IL-1β 5 Ok 0.962 0.841–1.10 0.567
IL-1RA 12 Ok 1.000 1.000–1.00 0.000 ***
IL-2 2 Sparse Data 0.701 0.545–0.90 0.006 **
IL-5 5 Ok 0.975 0.890–1.07 0.596
IL-6 12 Ok 0.999 0.999–1.00 0.018 *
IL-7 13 Ok 1.521 0.600–3.86 0.377
IL-8 (CXCL8) 13 Ok 0.998 0.996–1.00 0.061 .
IL-9 1 Sparse Data 0.769 0.550–1.08 0.125
IL-10 8 Ok 0.598 0.453–0.79 0.000 ***
IL-15 5 Ok 0.763 0.464–1.25 0.285
IL-17A/CTLA-8 1 Sparse Data 0.712 0.262–1.93 0.504
IL-18 13 Ok 1.009 0.998–1.02 0.107
IL-31 1 Sparse Data 1.023 0.868–1.21 0.786
IP-10 (CXCL10) 13 Ok 1.000 0.998–1.00 0.785
MCP-1 (CCL2) 13 Ok 0.999 0.999–1.00 0.000 ***
MIP-1α (CCL3) 13 Ok 0.952 0.885–1.02 0.179
MIP-1β (CCL4) 13 Ok 0.982 0.968–1.00 0.010 **
RANTES (CCL5) 13 Ok 1.000 0.958–1.04 0.987
SDF-1α 13 Ok 1.000 0.998–1.00 0.882
TNF-α 2 Sparse Data 0.610 0.464–0.80 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 14 Ok 1.030 0.921–1.15 0.608
GM-CSF 9 Ok 0.922 0.850–1.00 0.051 .
GRO-alpha (CXCL1) 14 Ok 0.995 0.990–1.00 0.028 *
IL-1α 1 Sparse Data 0.927 0.076–11.28 0.953
IL-1β 6 Ok 0.897 0.775–1.04 0.149
IL-1RA 13 Ok 1.000 1.000–1.00 0.059 .
IL-2 2 Sparse Data 0.589 0.475–0.73 0.000 ***
IL-5 6 Ok 0.960 0.863–1.07 0.444
IL-6 13 Ok 0.999 0.998–1.00 0.039 *
IL-7 14 Ok 1.251 0.425–3.69 0.685
IL-8 (CXCL8) 14 Ok 1.000 0.998–1.00 0.444
IL-9 1 Sparse Data 1.138 0.795–1.63 0.480
IL-10 9 Ok 0.895 0.717–1.12 0.328
IL-15 6 Ok 0.500 0.326–0.77 0.002 **
IL-17A/CTLA-8 2 Sparse Data 1.002 0.422–2.38 0.996
IL-18 14 Ok 1.008 0.997–1.02 0.150
IL-31 1 Sparse Data 0.951 0.792–1.14 0.589
IP-10 (CXCL10) 14 Ok 1.000 0.999–1.00 0.803
MCP-1 (CCL2) 14 Ok 1.000 0.999–1.00 0.727
MIP-1α (CCL3) 14 Ok 0.915 0.848–0.99 0.020 *
MIP-1β (CCL4) 14 Ok 0.991 0.975–1.01 0.286
RANTES (CCL5) 14 Ok 1.007 0.957–1.06 0.783
SDF-1α 14 Ok 1.000 1.000–1.00 0.895
TNF-α 3 Sparse Data 0.565 0.403–0.79 0.001 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 14 Ok 1.026 0.924–1.14 0.634
GM-CSF 9 Ok 0.928 0.861–1.00 0.054 .
GRO-alpha (CXCL1) 14 Ok 0.995 0.991–1.00 0.033 *
IL-1α 1 Sparse Data 0.553 0.057–5.32 0.608
IL-1β 6 Ok 0.905 0.791–1.04 0.149
IL-1RA 13 Ok 1.000 1.000–1.00 0.009 **
IL-2 2 Sparse Data 0.612 0.498–0.75 0.000 ***
IL-5 6 Ok 0.969 0.877–1.07 0.529
IL-6 13 Ok 0.999 0.999–1.00 0.034 *
IL-7 14 Ok 1.263 0.464–3.43 0.648
IL-8 (CXCL8) 14 Ok 0.999 0.998–1.00 0.246
IL-9 1 Sparse Data 1.076 0.772–1.50 0.666
IL-10 9 Ok 0.882 0.723–1.08 0.220
IL-15 6 Ok 0.557 0.357–0.87 0.010 **
IL-17A/CTLA-8 2 Sparse Data 0.916 0.414–2.03 0.829
IL-18 14 Ok 1.008 0.997–1.02 0.153
IL-31 1 Sparse Data 0.958 0.809–1.13 0.614
IP-10 (CXCL10) 14 Ok 1.000 0.999–1.00 0.897
MCP-1 (CCL2) 14 Ok 1.000 0.999–1.00 0.984
MIP-1α (CCL3) 14 Ok 0.920 0.859–0.98 0.017 *
MIP-1β (CCL4) 14 Ok 0.989 0.975–1.00 0.149
RANTES (CCL5) 14 Ok 1.007 0.960–1.06 0.778
SDF-1α 14 Ok 1.000 1.000–1.00 0.837
TNF-α 3 Sparse Data 0.583 0.434–0.78 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.319 0.91–1.91 0.141
GM-CSF 10 Ok 0.721 0.35–1.47 0.369
GRO-alpha (CXCL1) 15 Ok 0.534 0.28–1.01 0.054 .
IL-1α 1 Sparse Data 0.662 0.51–0.86 0.003 **
IL-1β 6 Ok 0.965 0.52–1.78 0.909
IL-1RA 14 Ok 0.471 0.32–0.68 0.000 ***
IL-2 2 Sparse Data 0.572 0.36–0.90 0.015 *
IL-5 7 Ok 0.831 0.39–1.77 0.631
IL-6 14 Ok 0.510 0.28–0.93 0.027 *
IL-7 15 Ok 1.186 0.73–1.92 0.488
IL-8 (CXCL8) 15 Ok 0.785 0.53–1.16 0.227
IL-9 1 Sparse Data 0.630 0.46–0.86 0.003 **
IL-10 9 Ok 1.071 0.68–1.69 0.769
IL-15 7 Ok 0.657 0.43–1.01 0.055 .
IL-17A/CTLA-8 2 Sparse Data 1.268 0.88–1.84 0.209
IL-18 15 Ok 1.673 0.95–2.96 0.077 .
IL-31 1 Sparse Data 1.164 0.65–2.10 0.613
IP-10 (CXCL10) 15 Ok 1.200 0.86–1.68 0.286
MCP-1 (CCL2) 15 Ok 0.492 0.30–0.82 0.006 **
MIP-1α (CCL3) 15 Ok 0.827 0.45–1.52 0.541
MIP-1β (CCL4) 15 Ok 0.877 0.59–1.30 0.513
RANTES (CCL5) 15 Ok 1.001 0.67–1.50 0.996
SDF-1α 15 Ok 1.023 0.62–1.68 0.929
TNF-α 3 Sparse Data 0.558 0.32–0.97 0.038 *
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

AIM 1: Reservoirs Dynamic and Reservoirs Characteristics

Model Results: Longitudinal / state-level data

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify reservoir characteristics associated with dynamics
Associations between Reservoirs Measures and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 0.914 7.4e-01–1.1e+00 0.414
HIV DNA (destination) Ok 1.005 8.5e-01–1.2e+00 0.954
Viral Diversity (source) Ok >1e6 5.6e+04–+Inf 0.006 **
Viral Diversity (destination) Ok >1e6 5.4e-02–+Inf 0.096 .
Viral Divergence Wide CI 3.318 <1e-6–+Inf 0.917
Associations between Reservoirs Measures and counts of migration events from the periphery to the CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 1.147 1.0e+00–1.3e+00 0.046 *
HIV DNA (destination) Ok 0.974 8.5e-01–1.1e+00 0.702
Viral Diversity (source) Ok >1e6 9.5e-01–+Inf 0.051 .
Viral Diversity (destination) Wide CI 0.054 <1e-6–+Inf 0.739
Viral Divergence Wide CI 0.002 <1e-6–+Inf 0.558
Associations between Reservoirs Measures and counts of migration events across the BBB
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 1.163 1.1e+00–1.3e+00 0.002 **
HIV DNA (destination) Ok 0.929 8.5e-01–1.0e+00 0.109
Viral Diversity (source) Ok >1e6 3.5e+04–+Inf 0.000 ***
Viral Diversity (destination) Ok 0.7 1.1e-06–4.3e+05 0.958
Viral Divergence Ok 0.015 <1e-6–4.2e+05 0.633
Associations between Reservoirs Measures and counts of migration events within CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 0.732 6.3e-01–8.5e-01 0.000 ***
HIV DNA (destination) Ok 1.061 9.4e-01–1.2e+00 0.345
Viral Diversity (source) Wide CI 140.794 <1e-6–+Inf 0.622
Viral Diversity (destination) Ok 37130.097 2.5e-03–+Inf 0.212
Viral Divergence Wide CI 4.294 <1e-6–+Inf 0.884

AIM 2: CNS Compartmentalization Analysis

Approach

Participants

  • All Last Gift individuals
  • Restricted to virally suppressed donors?
  • Only samples with SGA clones available in both CNS and periphery

1. Data Preparation

  • Multiple sequence alignment (MAFFT)
  • Build maximum-likelihood phylogeny (IQ-TREE)
    • GTR+Γ model and 1,000 ultrafast bootstraps

2. Identify CNS-specific Phylogenetic Clusters

  • Define CNS taxa per participant with custom R functions:
find_cns_clusters(tree, cns_taxa, min_size = 5)
filter_nested_clusters(cluster_list)
  • Scan all internal nodes. A cluster is retained if:
    • ≥ 5 tips - originate from CNS only
  • Nested CNS clades are automatically removed

3. Bayesian Temporal Reconstruction (BEAST)

  • Build time-scaled phylogenies for each participant
  • Model: Bayesian Skyline coalescent (MCMC chains ESS > 200)

4. Outputs

  • Time-scaled phylogeny annotated with CNS clusters
  • Posterior density of tMRCA for each CNS-specific cluster

Workflow

Results Summary

Note

  • Our analyses included 5 participants with sequences from both the CNS and Periphery.
  • Only SGA clones generated from tissue and body fluid samples were kept. (samples from Brain Myeloid cells and T cells will be integrated in future analyses)
🧠 Last Gift cohort HIV Reservoir CNS Clusters
cluster1 Cluster size tMRCA (median) lower HPD upper HPD Basal Ganglia Frontal Cortex (motor) Hippocampus Occipital Cortex Spinal Cord (cervical) Spinal Cord (lumbosacral) Spinal Cord (thoracic) Pons Medulla
LG03
Cluster 1 17 2005-10-26 1995-11-01 2011-12-05 4 (24%) 1 (6%) 2 (12%) 1 (6%) 3 (18%) 3 (18%) 3 (18%)
LG05
Cluster 1 17 2015-09-14 2012-10-21 2017-05-04 6 (35%) 5 (29%) 1 (6%) 1 (6%) 4 (24%)
LG14
Cluster 1 21 2014-06-04 2012-01-08 2016-03-26 7 (33%) 5 (24%) 4 (19%) 5 (24%)
LG16
Cluster 1 12 2012-10-05 2002-03-02 2017-05-15 7 (58%) 5 (42%)
LG18
Cluster 1 6 2006-01-15 1997-03-12 2013-06-26 6 (100%)
1 Using most recently single genome sequencing env data

Phylogenies

Interpretation/Discussion

  • Early vs late CNS seeding
  • Evidence for CNS-restricted evolution
  • Within-host compartmentalization patterns

Thank you